Motion blur often limits the quality of photographs and can be caused by either the shaking of the camera or the movement of photographed objects (e.g., subject, passerby, props, etc.) in the scene. Modern cameras address the former case with image stabilization, where motion sensors control mechanical actuators that shift the sensor or camera lens element in real time during the exposure to compensate for the motion (shaking) of the camera, e.g. Canon. 2003. EF Lens Work III, The Eyes of EOS. Canon Inc. Lens Product Group. The use of image stabilization enables sharp hand-held photographs of still subjects at much longer shutter speed, thereby reducing image noise. Unfortunately, image stabilization only addresses camera motion and cannot help with moving objects in the subject scene or field of view.
One option is to remove the blur after the shot was taken using deconvolution. However, this raises several challenges. First, the typical motion-blur kernel is a line segment in the direction of motion, which corresponds to a box filter. This kernel severely attenuates high spatial frequencies and deconvolution quickly becomes ill-conditioned. Second, the length and direction of the blur kernel both depend on the motion and are therefore unknown and must be estimated. Finally, motion blur usually varies over the image since different objects or regions can have different motion, and segmentation must be used to separate image regions with different motion. These two later challenges lead most existing motion deblurring strategies to rely on multiple input images (see Bascle, B., Blake, A., and Zisserman, A., “Motion de-blurring and superresolution from an image sequence,” ECCV, 1996; Rav-Acha and Peleg, S., “Two motion-blurred images are better than one,” Pattern Recognition Letters, 2005; Zheng, M. S. J., “A slit scanning depth of route panorama from stationary blur,” Proc. IEEE Conf. Comput. Vision Pattern Recog., 2005; Bar, L., Berkels, B., Sapiro, G., and Rumpf, M., “A variational framework for simultaneous motion estimation and restoration of motion-blurred video,” ICCV, 2007; Ben-Ezra, M., and Nayar, S. K., “Motion-based motion deblurring,” PAMI, 2004; Yuan, L., Sun, J., Quan, L., and Shum, H., “Image deblurring with blurred/noisy image pairs,” SIGGRAPH, 2007.)
More recent methods attempt to remove blur from a single input image using natural image statistics (see Fergus, R., Singh, B., Hertzmann, A., Roweis, S., and Freeman, W., “Removing camera shake from a single photograph,” SIGGRAPH, 2006; Levin, A., “Blind motion deblurring using image statistics,” Advances in Neural Information Processing Systems (NIPS), 2006). While these techniques demonstrated impressive abilities, their performance is still far from perfect. Raskar et al. proposed a hardware approach that addresses the first challenge (Raskar, R., Agrawal, A., and Tubmlin, J., “Coded exposure photography: Motion deblurring using fluttered shutter,” ACM Transactions on Graphics, SIGGRAPH 2006 Conference Proceedings, Boston, Mass. vol. 25, pgs. 795-804). A fluttered shutter modifies the line segment kernel to achieve a more broad-band frequency response, which allows for dramatically improved deconvolution results. While the Raskar approach blocks half of the light, the improved kernel is well worth the tradeoff. However, this approach still requires the precise knowledge of motion segmentation boundaries and object velocities, an unsolved problem.